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118   Daniel J. Levitin

                bottom half of the students in the silence condition. Then if the result of the
                experiment was that the music listeners as a group tended to perform better on
                their next exam,one could argue that this was not because they listened to
                music while they studied,but because they were the better students to begin
                with.
                  Again,the theory behind random assignment is to have groups of subjects
                who start out the same. Ideally,each group will have similar distributions on
                every conceivable dimension—age,sex, ethnicity,IQ,and variables that you
                might not think are important,such as handedness,astrological sign,or favor-
                ite television show. Random assignment makes it unlikely that there will be
                any large systematic differences between the groups.
                  A similar design flaw would arise if the experimental conditions were different.
                For example,if the music-listening group studied in a well-lit room with win-
                dows,and the silence group studied in a dark,windowless basement,any dif-
                ference between the groups could be due to the different environments. The
                room conditions become confounded with the music-listening conditions,such
                that it is impossibletodeducewhich of thetwo is thecausalfactor.
                  Performing random assignment of subjects is straightforward. Conceptually,
                one wants to mix the subjects’ names or numbers thoroughly,then draw them
                outofahat. Realistically,oneofthe easiestwaystodothisistogeneratea
                different random number for each subject,and then sort the random numbers.
                If n equals the total number of subjects you have,and g equals the number of
                groups you are dividing them into,the first n/g subjects will comprise the first
                group,the next n/g will comprise the second group,and so on.
                  If the results of a controlled experiment indicate a difference between groups,
                the next question is whether these findings are generalizable. If your initial
                group of subjects (the large group,before you randomly assigned subjects to
                conditions) was also randomly selected (called random sampling or random selec-
                tion,as opposed to random assignment),this is a reasonable conclusion to draw.
                However,there are almost always some constraints on one’s initial choice of
                subjects,and this constrains generalizability. For example,if all the subjects
                you studied in your music-listening experiment lived in fraternities,the finding
                might not generalize to people who do not live in fraternities. If you want to be
                able to generalize to all college students,you would need to take a representa-
                tive sample of all college students. One way to do this is to choose your sub-
                jects randomly,such that each member of the population you are considering
                (college students) has an equal likelihood of being placed in the experiment.
                  There are some interesting issues in representative sampling that are beyond
                the scope of this chapter. For example,if you wanted to take a representative
                sample of all American college students and you chose American college stu-
                dents randomly,it is possible that you would be choosing several students
                from some of the larger colleges,such as the University of Michigan,and you
                might not choose any students at all from some of the smaller colleges,such as
                Bennington College; this would limit the applicability of your findings to the
                colleges that were represented in your sample. One solution is to conduct a
                stratified sample,in which you first randomly select colleges (making it just as
                likely that you’ll choose large and small colleges) and then randomly select the
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